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一种新的几何约束求解器的研究
Alternative TitleThe research on a novel geometric constraint solver
曹春红; 张斌; 李小琳; 王利民; 李文辉
Department机器人学研究室
Conference Name6th World Congress on Intelligent Control and Automation
Conference DateJune 21-23, 2006
Conference PlaceDalian, China
Author of SourceDalian Univ Technol, Northeastern Univ, Dalian Maritime Univ, Shanghai Baosight Software Co Ltd, IEEE Robot & Automat Soc, Natl Nat Sci Fdn China, Chinese Assoc Automat, IEEE Control Syst Soc, Beijing Chapter, Minist Educ China, Grandar Robot Co Ltd, Yokogawa Elect Corp, KC Wong Educ Fdn, Siemens Ltd
Source PublicationWCICA 2006: Sixth World Congress on Intelligent Control and Automation, Vols 1-12, Conference Proceedings
PublisherIEEE
Publication PlaceNEW YORK
2006
Pages3504-3508
Indexed ByEI ; CPCI(ISTP)
EI Accession number20071510541876
WOS IDWOS:000241773204007
Contribution Rank2
ISBN1-4244-0331-6
Keyword几何约束求解 群智能算法 粒子群算法 复合粒子群算法
Abstract在将几何约束问题的约束方程组转化为优化模型的时候,我们需要找到一种方法来跳出局部最优解,进而找到全局最优解。为了兼顾算法的快速性和全局性,我们考虑使用复合粒子群算法。粒子群算法是一种基于群智能方法的演化计算技术。在所有的进化算法中都包括控制其自身特性的启发式参数,这些参数通常是与特定的问题相关并由用户自己定义。合适的参数选择需要用户丰富的经验和对研究问题所提供信息的正确判断。更重要的是,这些启发式参数会影响到算法的收敛特性。但是即使是很有经验的用户也可能选择不恰当的参数,从而使问题得不到有效地解决,这就越来越需要对这些参数进行研究。所以本文将粒子群算法中的控制参数的选取也作为一个优化问题,从而用常规遗传算法来控制粒子群算法中的启发式参数,形成复合粒子群优化算法。并把复粒子群算法成功的应用到几何约束求解技术。
Other AbstractWhen transferring the geometric constraint equation group into the optimization model, we need a method to jump out of the local beat solution so that we can find a global best solution. Considering the speed and global capability, we adopt compound particle group optimization algorithm. Particle swarm optimization algorithm is a kind of evolution computation technology based on group intelligence. In all the evolution computations heuristic function should be included to control its one's own characteristic. These parameters are usually correlated with the specific problem and are defined by the users. Suitable parameter choice needs user abundant experience and correct judgment on the information offered by the problem. More important thing is that these heuristic parameters will influence the convergence characteristic of the algorithm. Because of this even experienced users may choose the not appropriate parameter and then make the problem unable to get effective solution. It needs to carry on some research on these parameters more and more. Here we choose the control parameters as an optimization question in the particle swarm algorithm. Thus heuristic function in the PSO can be controlled by the ordinal genetic algorithm and we form the composite particle swarm optimization algorithm. And we use this algorithm into the geometric constraint solving successfully.
Language中文
Citation statistics
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/20020
Collection机器人学研究室
Corresponding Author曹春红
Affiliation1.Collge of Information Science and Engineering, Northeastern University, Shenyang 110004, China
2.National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
3.College of Computer Science and Technology, Jilin University, Changchun 130012, China
4.Robotics Laboratory, Shenyang Institute of Automation, Chinese Academy of Science, Shenyang 110016
Recommended Citation
GB/T 7714
曹春红,张斌,李小琳,等. 一种新的几何约束求解器的研究[C]//Dalian Univ Technol, Northeastern Univ, Dalian Maritime Univ, Shanghai Baosight Software Co Ltd, IEEE Robot & Automat Soc, Natl Nat Sci Fdn China, Chinese Assoc Automat, IEEE Control Syst Soc, Beijing Chapter, Minist Educ China, Grandar Robot Co Ltd, Yokogawa Elect Corp, KC Wong Educ Fdn, Siemens Ltd. NEW YORK:IEEE,2006:3504-3508.
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